Via. computational methods. new drug discovery has been acknowledged as a complicated, expensive, time consuming, and challenging project. it has been estimated that around 12 years and 2.7 billion usd, on average, are demanded for a new drug discovery via traditional drug development pipeline. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. in this work, we discussed the different subareas of the computer aided drug discovery.
Anti cancer drugs, a section of the journal frontiers in pharmacology received: 12 march 2020 accepted: 01 may 2020 published: 20 may 2020 citation: cui w, aouidate a, wang s, yu q, li y and yuan s (2020) discovering anti cancer drugs via computational methods. front. pharmacol. 11:733. doi: 10.3389 fphar.2020.00733 review published: 20 may 2020. In this respect, computational methods could be constructive for performing different tasks including protein interaction network analysis, drug target prediction, binding site prediction, virtual screening, and many others. all these innovative methods could considerably facilitate the anti cancer drug discovery. It has been reported that the total investment required for discovering and developing new drugs averages usd 2 billion, the whole process takes years even decades. the cost for the anti cancer drug is much more than this. in recent years, computational drug development methods have emerged to address computational experimental challenges to add immediate value to drug development pipelines. The different subareas of the computer aided drug discovery process with a focus on anticancer drugs are discussed and fruitful insights are provided into the area of cancer therapy. new drug discovery has been acknowledged as a complicated, expensive, time consuming, and challenging project. it has been estimated that around 12 years and 2.7 billion usd, on average, are demanded for a new.
It has been reported that the total investment required for discovering and developing new drugs averages usd 2 billion, the whole process takes years even decades. the cost for the anti cancer drug is much more than this. in recent years, computational drug development methods have emerged to address computational experimental challenges to add immediate value to drug development pipelines. The different subareas of the computer aided drug discovery process with a focus on anticancer drugs are discussed and fruitful insights are provided into the area of cancer therapy. new drug discovery has been acknowledged as a complicated, expensive, time consuming, and challenging project. it has been estimated that around 12 years and 2.7 billion usd, on average, are demanded for a new. Key takeaway: 'computer aided drug discovery (cadd) technology offers faster, cheaper, and more effective anticancer drug design, reducing research costs and accelerating the development process.'. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. in this work, we discussed the different subareas of the computer aided drug discovery.
Key takeaway: 'computer aided drug discovery (cadd) technology offers faster, cheaper, and more effective anticancer drug design, reducing research costs and accelerating the development process.'. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. in this work, we discussed the different subareas of the computer aided drug discovery.
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